Data Integration Is Essential To Winning
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Data Integration Is Essential To Winning

Ruma Bhattacharyya, Vice President of Business Systems Analysis, Everest
Ruma Bhattacharyya, Vice President of Business Systems Analysis, Everest

Ruma Bhattacharyya, Vice President of Business Systems Analysis, Everest

For businesses today, data proliferation can be extremely valuable as it is a critical component for making prudent business decisions and gaining operational efficiency. Yet processing that amount of data and seamlessly incorporating it into new and existing workflows is not so easy, especially in highly regulated industries, as there are many common data integration challenges that businesses need to address. In order to benefit from the vast amount of data, it is essential for companies to have a solid data strategy in place. And proper data integration is the most important component of a data strategy plan.

Data Integration Challenges

Beginning with usability. Data is king, but it is just data if it can’t speak the same language or be translated for different workflows and departments. Businesses are often faced with inconsistent data formats and models which is where many of the data integration challenges arise. For example, within the insurance industry, the data received and saved by the policy management group is often different from how the claims operations team needs to present their data. To fix it, the data needs to be normalized so that it is compatible across the workflow. Once this usability challenge is identified, data normalization can be implemented either manually or through the implementation of a data analytics tool, and the data can then be utilized and integrated into the workflow more easily.

Next, there is the issue of quality and consistency. Unfortunately, manual data entry is still prevalent, and a lack of companywide standard practices can lead to inaccurate, inconsistent, outdated, and/or duplicative data. To prevent this, data quality management – where someone is assigned to validate the data before it gets added into the system – must be enforced. Metadata management is another aspect of overseeing data quality that when handled incorrectly can pose serious challenges. Mismanagement can create an inability to share metadata across different applications, resulting in inefficient and error-prone processes. In fact, Gartner estimates that poor data quality costs organizations an average of $12.9 million every year.

"Strategically integrating data throughout your workflow not only provides a 360-degree view of your business but can also enable you to make smarter and more informed business decisions, as well as build operational efficiency"

Data silos are another common pitfall for data integration. Data silos are created when data is accessible by one department but not easily available to other groups. When data is scattered through the enterprise, the risk of missing a crucial part of data is always present. Proper workflow integration, which connects one application with another typically via the Application Programming Interfaces (APIs), can help break down these silos. By putting APIs into place, users can do data entry in one application and data can move between applications. This can increase employee productivity and minimize costly human errors.

Best Practices for Seamless Data Integration

While there are specific, targeted solutions for each of these challenges, the businesses best positioned to gain a competitiveadvantage are those that have evaluated, developed and implemented a seamless data integration strategy. Based on my experience within the insurance industry, there are three key areas that I recommend focusing on to effectively embed data and analytics throughout the workflow.

1. Always Start With Talent

Onboarding a new employee is arguably the most important process for the human resources team. Connecting the Human Resource Information System (HRIS), like Workday, with an IT Service Management (ITSM) system, like ServiceNow that both use the same personnel data can help ensure this activity gets carried out smoothly. For example, when an employee is marked as hired in the Workday system, specific onboarding tickets will automatically get created in the ServiceNow system. From there IT employees will be notified enabling them to move quickly to start getting the new hire’s computer, email, phone and other IT needs set up before the new employee’s first day.

2. Identify and Implement Efficiencies

For the insurance industry, expediting the time it takes to settle claims is imperative to a positive customer experience and therefore crucial to the success of the overall business. When it comes to the claims management process, one of the critical initial steps is to gather and process relevant information regarding the underlying policy and coverage and compare it to the claim at hand. By making insurance policy information more readily available to the claims management system, underlying policy and coverages can be verified as soon as a claim is filed, which can significantly accelerate the claims processing time – enabling the customer to get their claim payout sooner.

3. Automate Analytics

Highly regulated industries have always relied upon mathematical models and data analytics (actuarial science) to predict risks. And companies that successfully integrate business intelligence platforms into their data workflow will see immediate benefits. For insurance carriers, for example, not only can the use of modeling and analytics improve the predictions for underwriting and risk management, but it can also help to identify and reduce fraud, provide a fuller picture of risk to improve decision making, and identify ways to better serve customers enabling the delivery of a superior customer experience.

Strategically integrating data throughout your workflow not only provides a 360-degree view of your business but can also enable you to make smarter and more informed business decisions, as well as build operational efficiency. And any time your speed and accuracy improve so does your customer’s experience and satisfaction in working with you. With the World Economic Forum predicting that by 2025, “the amount of data generated each day will reach 463 exabytes globally,” now is the time for companies to re-evaluate their data integration and analytics models and make sure they’re properly integrated throughout their business. Companies with the strategic infrastructure in place to collect, process, and analyze all the relevant and available data and integrate it effortlessly into their workflows are those that will be best positioned to compete and succeed in today’s crowded marketplace.

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